--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: physiological metabolisms of seaweeds usually suffered climate changes in the field. gracilariopsis lemaneiformis and ulva lactuca, collected from nan ao island, shantou, china, were cultured under ambient and elevated co2 supply , with low and high temperatures for weeks, aiming to compare the difference of the main physiological metabolism between two seaweed species in response to the elevated co2 and high temperature. at 15 , the ph reduction in the culture medium caused by elevated co2 was larger in . lemaneiformis than in . lactuca. at 25 , elevated co2 significantly increased photosynthetic rates and maintained constant respiratory rates in . lemaneiformis. however, for 25 grown . lactuca, the increment of co2 did not enhance the pn rates but rapidly decreased the rd rates itself. with the higher rd pg ratios in . lemaneiformis than . lactuca, the warming thereby promoted more allocation of photosynthetic products to respiratory consumption in . lemaneiformis. both pg and rd rates exhibited lower temperature acclimation in two seaweeds. in addition, elevated co2 markedly increased the relative growth rate and phycobiliprotein contents at 25 , but exhibited no enhancement of chlorophyll , carotenoids , soluble carbohydrate , and soluble protein contents in . lemaneiformis, with the reduction of sc when temperature increased only. we suggested that climate changes were probably more benefit to . lactuca than to . lemaneiformis, inherently justifying the metabolism during . lemaneiformis maricultivation. 2018, springer verlag gmbh germany, part of springer nature. - text: blue carbon is vital aspect of climate change mitigation, which necessitates the identification of stocks and drivers for implementing mitigation strategies. however, reclamation may be among the most invasive forms, and the question of its influence has not been addressed well in blue carbon research. therefore, the effects of reclamation on carbon stocks and the interaction of crucial drivers from reclamation time areas were evaluated in the liaohe river delta and compared with natural reserves . carbon stocks based on invest model were lower than preexisting conditions . one way analysis of variance showed that average carbon stocks accumulated 55 years after reclamation and reached the lowest value in 85 years. the interaction analysis of dominant drivers affecting carbon showed the difference between reclaimed areas and reserves regarding potential effect pathways. in the 1930s and 1960s reclamation time areas, crop yield and industrial output determined blue carbon by changing no3 and ap. in the 1990s reclamation time area, population density played an important role. in defining the impact of vegetation cover on carbon within the reserves, the distance to the coast and residence were significant factors. this study demonstrated that coastal - text: multiple techniques, including thermal infrared aerial remote sensing, geophysical and geological data, geochemical characterization and radium isotopes, were used to evaluate the role of groundwater as source of dissolved nutrients, carbon, and trace gases to the okatee river estuary, south carolina. thermal infrared aerial remote sensing surveys illustrated the presence of multiple submarine groundwater discharge sites in okatee headwaters. significant relationships were observed between groundwater geochemical constituents and ra 226 activity in groundwater with higher ra 226 activity correlated to higher concentrations of organics, dissolved inorganic carbon, nutrients, and trace gases to the okatee system. system level radium mass balance confirmed substantial submarine groundwater discharge contribution of these constituents to the okatee river. diffusive benthic flux measurements and potential denitrification rate assays tracked the fate of constituents in creek bank sediments. diffusive benthic fluxes were substantially lower than calculated radium based submarine groundwater discharge inputs, showing that advection of groundwater derived nutrients dominated fluxes in the system. while considerable potential for denitrification in tidal creek bank sediments was noted, in situ denitrification rates were nitrate limited, making intertidal sediments an inefficient nitrogen sink in this system. groundwater geochemical data indicated significant differences in groundwater chemical composition and radium activity ratios between the eastern and western sides of the river; these likely arose from the distinct hydrological regimes observed in each area. groundwater from the western side of the okatee headwaters was characterized by higher concentrations of dissolved organic and inorganic carbon, dissolved organic nitrogen, inorganic nutrients and reduced metabolites and trace gases, .. methane and nitrous oxide, than groundwater from the eastern side. differences in microbial sulfate reduction, organic matter supply, and or groundwater residence time likely contributed to this pattern. the contrasting features of the east and west sub marsh zones highlight the need for multiple techniques for characterization of submarine groundwater discharge sources and the impact of biogeochemical processes on the delivery of nutrients and carbon to coastal areas via submarine groundwater discharge. 2014 elsevier ltd. all rights reserved. - text: blue carbon ecosystem initiatives in the coral triangle region are increasing due to their amplified recognition in mitigating global climate change. although transdisciplinary approaches in the blue carbon discourse and collaborative actions are gaining momentum in the international and national arenas, more work is still needed at the local level. the study pursues how bce initiatives permeate through the local communities in the philippines and indonesia, as part of ctr. using perception surveys, the coastal residents from busuanga, philippines, and karimunjawa, indonesia were interviewed on their awareness, utilization, perceived threats, and management strategies for bces. potential factors affecting residents perceptions were explored using multivariate regression and correlation analyses. also, comparative analysis was done to determine distinctions and commonalities in perceptions as influenced by site specific scenarios. results show that, despite respondents presenting relatively high awareness of bce services, levels of utilization are low with 42. 92. and 23. 85. respondents in busuanga and karimunjawa, respectively, not directly utilizing bce resources. regression analysis showed that respondents occupation significantly influenced their utilization rate and observed opposite correlations in busuanga and karimunjawa . perceived threats are found to be driven by personal experiences occurrence of natural disasters in busuanga whereas discerned anthropogenic activities in karimunjawa. meanwhile, recognized management strategies are influenced by the strong presence of relevant agencies like non government and people organizations in busuanga and the local government in karimunjawa. these results can be translated as useful metrics in contextualizing and or enhancing bce management plans specifically in strategizing advocacy campaigns and engagement of local stakeholders across the ctr. - text: mangrove wetlands are important ecosystems, yet human development coupled with climate change threatens mangroves and their large carbon stores. this study seeks to understand the soil carbon dynamics in hydrologically altered mangrove swamps by studying aboveground biomass estimates and belowground soil carbon concentrations in mangrove swamps with high, medium, and low levels of disturbance in catano, jobos bay, and vieques, puerto rico. all three sites were affected by hurricane maria in 2017, one year prior to the study. as result of being hit by the saffir simpson category hurricane, the low disturbance site had almost no living mangroves left during sampling. there was no correlation between level of hydrologic alteration and carbon storage, rather different patterns emerged for each of the three sites. at the highly disturbed location, belowground carbon mass averaged .048 .001 cm which increased with increased aboveground biomass. at the moderately disturbed location, belowground carbon mass averaged .047 .003 cm and corresponded to distance from open water. at the low disturbed location, organic carbon was consistent between all sites and inorganic carbon concentrations controlled total carbon mass which averaged .048 .002 cm. these results suggest that mangroves are adaptive and resilient and have the potential to retain their carbon storage capacities despite hydrologic alterations, but mass carbon storage within mangrove forests can be spatially variable in hydrologically altered conditions. pipeline_tag: text-classification inference: false base_model: sentence-transformers/paraphrase-mpnet-base-v2 --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a MultiOutputClassifier instance - **Maximum Sequence Length:** 512 tokens ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("ignaciosg/blueCarbon") # Run inference preds = model("blue carbon is vital aspect of climate change mitigation, which necessitates the identification of stocks and drivers for implementing mitigation strategies. however, reclamation may be among the most invasive forms, and the question of its influence has not been addressed well in blue carbon research. therefore, the effects of reclamation on carbon stocks and the interaction of crucial drivers from reclamation time areas were evaluated in the liaohe river delta and compared with natural reserves . carbon stocks based on invest model were lower than preexisting conditions . one way analysis of variance showed that average carbon stocks accumulated 55 years after reclamation and reached the lowest value in 85 years. the interaction analysis of dominant drivers affecting carbon showed the difference between reclaimed areas and reserves regarding potential effect pathways. in the 1930s and 1960s reclamation time areas, crop yield and industrial output determined blue carbon by changing no3 and ap. in the 1990s reclamation time area, population density played an important role. in defining the impact of vegetation cover on carbon within the reserves, the distance to the coast and residence were significant factors. this study demonstrated that coastal") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 105 | 229.475 | 432 | ### Training Hyperparameters - batch_size: (1, 1) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.0006155918397454662 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - max_length: 1000 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0000 | 1 | 0.1819 | - | | 0.0011 | 50 | 0.201 | - | | 0.0023 | 100 | 0.3533 | - | | 0.0034 | 150 | 0.0788 | - | | 0.0046 | 200 | 0.1445 | - | | 0.0057 | 250 | 0.1584 | - | | 0.0069 | 300 | 0.3425 | - | | 0.0080 | 350 | 0.1203 | - | | 0.0092 | 400 | 0.2045 | - | | 0.0103 | 450 | 0.0287 | - | | 0.0115 | 500 | 0.1784 | - | | 0.0126 | 550 | 0.2521 | - | | 0.0138 | 600 | 0.1285 | - | | 0.0149 | 650 | 0.2292 | - | | 0.0161 | 700 | 0.0943 | - | | 0.0172 | 750 | 0.1753 | - | | 0.0184 | 800 | 0.3433 | - | | 0.0195 | 850 | 0.262 | - | | 0.0207 | 900 | 0.1097 | - | | 0.0218 | 950 | 0.0015 | - | | 0.0230 | 1000 | 0.5522 | - | | 0.0241 | 1050 | 0.5939 | - | | 0.0253 | 1100 | 0.1134 | - | | 0.0264 | 1150 | 0.1258 | - | | 0.0276 | 1200 | 0.0146 | - | | 0.0287 | 1250 | 0.0467 | - | | 0.0299 | 1300 | 0.3501 | - | | 0.0310 | 1350 | 0.291 | - | | 0.0322 | 1400 | 0.0569 | - | | 0.0333 | 1450 | 0.0812 | - | | 0.0345 | 1500 | 0.3397 | - | | 0.0356 | 1550 | 0.1664 | - | | 0.0368 | 1600 | 0.3841 | - | | 0.0379 | 1650 | 0.1659 | - | | 0.0391 | 1700 | 0.0809 | - | | 0.0402 | 1750 | 0.3604 | - | | 0.0414 | 1800 | 0.0056 | - | | 0.0425 | 1850 | 0.3335 | - | | 0.0437 | 1900 | 0.0005 | - | | 0.0448 | 1950 | 0.1624 | - | | 0.0460 | 2000 | 0.8162 | - | | 0.0471 | 2050 | 0.0097 | - | | 0.0483 | 2100 | 0.2561 | - | | 0.0494 | 2150 | 0.0003 | - | | 0.0506 | 2200 | 0.4198 | - | | 0.0517 | 2250 | 0.0002 | - | | 0.0529 | 2300 | 0.2793 | - | | 0.0540 | 2350 | 0.6288 | - | | 0.0552 | 2400 | 0.6944 | - | | 0.0563 | 2450 | 0.7394 | - | | 0.0575 | 2500 | 0.011 | - | | 0.0586 | 2550 | 0.8041 | - | | 0.0598 | 2600 | 0.0041 | - | | 0.0609 | 2650 | 0.2446 | - | | 0.0621 | 2700 | 0.2759 | - | | 0.0632 | 2750 | 0.151 | - | | 0.0644 | 2800 | 0.0651 | - | | 0.0655 | 2850 | 0.0026 | - | | 0.0666 | 2900 | 0.0845 | - | | 0.0678 | 2950 | 0.7541 | - | | 0.0689 | 3000 | 0.0993 | - | | 0.0701 | 3050 | 0.7355 | - | | 0.0712 | 3100 | 0.6959 | - | | 0.0724 | 3150 | 0.1687 | - | | 0.0735 | 3200 | 0.2048 | - | | 0.0747 | 3250 | 0.0906 | - | | 0.0758 | 3300 | 0.0582 | - | | 0.0770 | 3350 | 0.9064 | - | | 0.0781 | 3400 | 0.8038 | - | | 0.0793 | 3450 | 0.2515 | - | | 0.0804 | 3500 | 0.0196 | - | | 0.0816 | 3550 | 0.0081 | - | | 0.0827 | 3600 | 0.8483 | - | | 0.0839 | 3650 | 0.0651 | - | | 0.0850 | 3700 | 0.8224 | - | | 0.0862 | 3750 | 0.2872 | - | | 0.0873 | 3800 | 0.0506 | - | | 0.0885 | 3850 | 0.6795 | - | | 0.0896 | 3900 | 0.0126 | - | | 0.0908 | 3950 | 0.5083 | - | | 0.0919 | 4000 | 0.0215 | - | | 0.0931 | 4050 | 0.8133 | - | | 0.0942 | 4100 | 0.1534 | - | | 0.0954 | 4150 | 0.2397 | - | | 0.0965 | 4200 | 0.8576 | - | | 0.0977 | 4250 | 0.0554 | - | | 0.0988 | 4300 | 0.1018 | - | | 0.1000 | 4350 | 0.3324 | - | | 0.1011 | 4400 | 0.0221 | - | | 0.1023 | 4450 | 0.0516 | - | | 0.1034 | 4500 | 0.796 | - | | 0.1046 | 4550 | 0.0903 | - | | 0.1057 | 4600 | 0.1979 | - | | 0.1069 | 4650 | 0.9194 | - | | 0.1080 | 4700 | 0.2556 | - | | 0.1092 | 4750 | 0.7224 | - | | 0.1103 | 4800 | 0.0012 | - | | 0.1115 | 4850 | 0.5042 | - | | 0.1126 | 4900 | 0.5732 | - | | 0.1138 | 4950 | 0.1041 | - | | 0.1149 | 5000 | 0.0247 | - | | 0.1161 | 5050 | 0.0265 | - | | 0.1172 | 5100 | 0.0126 | - | | 0.1184 | 5150 | 0.0098 | - | | 0.1195 | 5200 | 0.0386 | - | | 0.1207 | 5250 | 0.001 | - | | 0.1218 | 5300 | 0.9248 | - | | 0.1230 | 5350 | 0.4783 | - | | 0.1241 | 5400 | 0.1841 | - | | 0.1253 | 5450 | 0.4721 | - | | 0.1264 | 5500 | 0.0601 | - | | 0.1276 | 5550 | 0.0073 | - | | 0.1287 | 5600 | 0.0028 | - | | 0.1298 | 5650 | 0.012 | - | | 0.1310 | 5700 | 0.0451 | - | | 0.1321 | 5750 | 0.0125 | - | | 0.1333 | 5800 | 0.5423 | - | | 0.1344 | 5850 | 0.7545 | - | | 0.1356 | 5900 | 0.0158 | - | | 0.1367 | 5950 | 0.1388 | - | | 0.1379 | 6000 | 0.0136 | - | | 0.1390 | 6050 | 0.0043 | - | | 0.1402 | 6100 | 0.4147 | - | | 0.1413 | 6150 | 0.0503 | - | | 0.1425 | 6200 | 0.0347 | - | | 0.1436 | 6250 | 0.0465 | - | | 0.1448 | 6300 | 0.0086 | - | | 0.1459 | 6350 | 0.8752 | - | | 0.1471 | 6400 | 0.5546 | - | | 0.1482 | 6450 | 0.0348 | - | | 0.1494 | 6500 | 0.0853 | - | | 0.1505 | 6550 | 0.6107 | - | | 0.1517 | 6600 | 0.005 | - | | 0.1528 | 6650 | 0.3526 | - | | 0.1540 | 6700 | 0.2429 | - | | 0.1551 | 6750 | 0.6727 | - | | 0.1563 | 6800 | 0.0019 | - | | 0.1574 | 6850 | 0.6662 | - | | 0.1586 | 6900 | 0.0068 | - | | 0.1597 | 6950 | 0.0117 | - | | 0.1609 | 7000 | 0.4718 | - | | 0.1620 | 7050 | 0.0072 | - | | 0.1632 | 7100 | 0.8174 | - | | 0.1643 | 7150 | 0.0094 | - | | 0.1655 | 7200 | 0.0241 | - | | 0.1666 | 7250 | 0.1359 | - | | 0.1678 | 7300 | 0.0528 | - | | 0.1689 | 7350 | 0.0184 | - | | 0.1701 | 7400 | 0.2204 | - | | 0.1712 | 7450 | 0.3476 | - | | 0.1724 | 7500 | 0.1153 | - | | 0.1735 | 7550 | 0.0717 | - | | 0.1747 | 7600 | 0.022 | - | | 0.1758 | 7650 | 0.0311 | - | | 0.1770 | 7700 | 0.4385 | - | | 0.1781 | 7750 | 0.4274 | - | | 0.1793 | 7800 | 0.4994 | - | | 0.1804 | 7850 | 0.2518 | - | | 0.1816 | 7900 | 0.8652 | - | | 0.1827 | 7950 | 0.0019 | - | | 0.1839 | 8000 | 0.01 | - | | 0.1850 | 8050 | 0.0129 | - | | 0.1862 | 8100 | 0.0001 | - | | 0.1873 | 8150 | 0.0005 | - | | 0.1885 | 8200 | 0.0199 | - | | 0.1896 | 8250 | 0.1489 | - | | 0.1908 | 8300 | 0.0016 | - | | 0.1919 | 8350 | 0.5111 | - | | 0.1931 | 8400 | 0.807 | - | | 0.1942 | 8450 | 0.1489 | - | | 0.1953 | 8500 | 0.29 | - | | 0.1965 | 8550 | 0.0001 | - | | 0.1976 | 8600 | 0.0043 | - | | 0.1988 | 8650 | 0.0041 | - | | 0.1999 | 8700 | 0.3061 | - | | 0.2011 | 8750 | 0.0221 | - | | 0.2022 | 8800 | 0.801 | - | | 0.2034 | 8850 | 0.2316 | - | | 0.2045 | 8900 | 0.2784 | - | | 0.2057 | 8950 | 0.0957 | - | | 0.2068 | 9000 | 0.611 | - | | 0.2080 | 9050 | 0.7529 | - | | 0.2091 | 9100 | 0.0565 | - | | 0.2103 | 9150 | 0.0114 | - | | 0.2114 | 9200 | 0.2864 | - | | 0.2126 | 9250 | 0.1954 | - | | 0.2137 | 9300 | 0.7993 | - | | 0.2149 | 9350 | 0.0501 | - | | 0.2160 | 9400 | 0.0051 | - | | 0.2172 | 9450 | 0.6012 | - | | 0.2183 | 9500 | 0.0131 | - | | 0.2195 | 9550 | 0.0157 | - | | 0.2206 | 9600 | 0.0606 | - | | 0.2218 | 9650 | 0.9143 | - | | 0.2229 | 9700 | 0.0001 | - | | 0.2241 | 9750 | 0.0021 | - | | 0.2252 | 9800 | 0.0004 | - | | 0.2264 | 9850 | 0.0498 | - | | 0.2275 | 9900 | 0.0021 | - | | 0.2287 | 9950 | 0.8591 | - | | 0.2298 | 10000 | 0.2218 | - | | 0.2310 | 10050 | 0.0065 | - | | 0.2321 | 10100 | 0.0924 | - | | 0.2333 | 10150 | 0.8866 | - | | 0.2344 | 10200 | 0.0004 | - | | 0.2356 | 10250 | 0.1434 | - | | 0.2367 | 10300 | 0.0118 | - | | 0.2379 | 10350 | 0.025 | - | | 0.2390 | 10400 | 0.8472 | - | | 0.2402 | 10450 | 0.0352 | - | | 0.2413 | 10500 | 0.0105 | - | | 0.2425 | 10550 | 0.0025 | - | | 0.2436 | 10600 | 0.0042 | - | | 0.2448 | 10650 | 0.3461 | - | | 0.2459 | 10700 | 0.0314 | - | | 0.2471 | 10750 | 0.1411 | - | | 0.2482 | 10800 | 0.0006 | - | | 0.2494 | 10850 | 0.0013 | - | | 0.2505 | 10900 | 0.894 | - | | 0.2517 | 10950 | 0.9961 | - | | 0.2528 | 11000 | 0.9908 | - | | 0.2540 | 11050 | 0.836 | - | | 0.2551 | 11100 | 0.8847 | - | | 0.2563 | 11150 | 0.8493 | - | | 0.2574 | 11200 | 0.5851 | - | | 0.2585 | 11250 | 0.9502 | - | | 0.2597 | 11300 | 0.8396 | - | | 0.2608 | 11350 | 0.1942 | - | | 0.2620 | 11400 | 0.9298 | - | | 0.2631 | 11450 | 0.742 | - | | 0.2643 | 11500 | 0.8624 | - | | 0.2654 | 11550 | 0.5423 | - | | 0.2666 | 11600 | 0.8576 | - | | 0.2677 | 11650 | 0.8042 | - | | 0.2689 | 11700 | 0.7447 | - | | 0.2700 | 11750 | 0.5319 | - | | 0.2712 | 11800 | 0.451 | - | | 0.2723 | 11850 | 0.4115 | - | | 0.2735 | 11900 | 0.6772 | - | | 0.2746 | 11950 | 0.4701 | - | | 0.2758 | 12000 | 0.6101 | - | | 0.2769 | 12050 | 0.4914 | - | | 0.2781 | 12100 | 0.653 | - | | 0.2792 | 12150 | 0.6205 | - | | 0.2804 | 12200 | 0.651 | - | | 0.2815 | 12250 | 0.2223 | - | | 0.2827 | 12300 | 0.7124 | - | | 0.2838 | 12350 | 0.6502 | - | | 0.2850 | 12400 | 0.5812 | - | | 0.2861 | 12450 | 0.6483 | - | | 0.2873 | 12500 | 0.7335 | - | | 0.2884 | 12550 | 0.239 | - | | 0.2896 | 12600 | 0.6499 | - | | 0.2907 | 12650 | 0.4453 | - | | 0.2919 | 12700 | 0.7152 | - | | 0.2930 | 12750 | 0.5551 | - | | 0.2942 | 12800 | 0.6034 | - | | 0.2953 | 12850 | 0.5714 | - | | 0.2965 | 12900 | 0.5867 | - | | 0.2976 | 12950 | 0.4249 | - | | 0.2988 | 13000 | 0.7262 | - | | 0.2999 | 13050 | 0.542 | - | | 0.3011 | 13100 | 0.5301 | - | | 0.3022 | 13150 | 0.7503 | - | | 0.3034 | 13200 | 0.6918 | - | | 0.3045 | 13250 | 0.5352 | - | | 0.3057 | 13300 | 0.6065 | - | | 0.3068 | 13350 | 0.373 | - | | 0.3080 | 13400 | 0.7648 | - | | 0.3091 | 13450 | 0.2762 | - | | 0.3103 | 13500 | 0.708 | - | | 0.3114 | 13550 | 0.1481 | - | | 0.3126 | 13600 | 0.7231 | - | | 0.3137 | 13650 | 0.6023 | - | | 0.3149 | 13700 | 0.7021 | - | | 0.3160 | 13750 | 0.5843 | - | | 0.3172 | 13800 | 0.7361 | - | | 0.3183 | 13850 | 0.7844 | - | | 0.3195 | 13900 | 0.51 | - | | 0.3206 | 13950 | 0.506 | - | | 0.3218 | 14000 | 0.3072 | - | | 0.3229 | 14050 | 0.5854 | - | | 0.3240 | 14100 | 0.3553 | - | | 0.3252 | 14150 | 0.6827 | - | | 0.3263 | 14200 | 0.5342 | - | | 0.3275 | 14250 | 0.6887 | - | | 0.3286 | 14300 | 0.6007 | - | | 0.3298 | 14350 | 0.4573 | - | | 0.3309 | 14400 | 0.5979 | - | | 0.3321 | 14450 | 0.5328 | - | | 0.3332 | 14500 | 0.6814 | - | | 0.3344 | 14550 | 0.6207 | - | | 0.3355 | 14600 | 0.8189 | - | | 0.3367 | 14650 | 0.5794 | - | | 0.3378 | 14700 | 0.3987 | - | | 0.3390 | 14750 | 0.5281 | - | | 0.3401 | 14800 | 0.652 | - | | 0.3413 | 14850 | 0.6811 | - | | 0.3424 | 14900 | 0.3334 | - | | 0.3436 | 14950 | 0.565 | - | | 0.3447 | 15000 | 0.4956 | - | | 0.3459 | 15050 | 0.7289 | - | | 0.3470 | 15100 | 0.6103 | - | | 0.3482 | 15150 | 0.4173 | - | | 0.3493 | 15200 | 0.2138 | - | | 0.3505 | 15250 | 0.893 | - | | 0.3516 | 15300 | 0.5385 | - | | 0.3528 | 15350 | 0.6386 | - | | 0.3539 | 15400 | 0.7168 | - | | 0.3551 | 15450 | 0.1189 | - | | 0.3562 | 15500 | 0.3046 | - | | 0.3574 | 15550 | 0.4776 | - | | 0.3585 | 15600 | 0.7062 | - | | 0.3597 | 15650 | 0.0972 | - | | 0.3608 | 15700 | 0.4485 | - | | 0.3620 | 15750 | 0.5843 | - | | 0.3631 | 15800 | 0.5656 | - | | 0.3643 | 15850 | 0.5682 | - | | 0.3654 | 15900 | 0.416 | - | | 0.3666 | 15950 | 0.2427 | - | | 0.3677 | 16000 | 0.4942 | - | | 0.3689 | 16050 | 0.4734 | - | | 0.3700 | 16100 | 0.7099 | - | | 0.3712 | 16150 | 0.5899 | - | | 0.3723 | 16200 | 0.3502 | - | | 0.3735 | 16250 | 0.3448 | - | | 0.3746 | 16300 | 0.6606 | - | | 0.3758 | 16350 | 0.5239 | - | | 0.3769 | 16400 | 0.6872 | - | | 0.3781 | 16450 | 0.2828 | - | | 0.3792 | 16500 | 0.6973 | - | | 0.3804 | 16550 | 0.6628 | - | | 0.3815 | 16600 | 0.6429 | - | | 0.3827 | 16650 | 0.4321 | - | | 0.3838 | 16700 | 0.6626 | - | | 0.3850 | 16750 | 0.5044 | - | | 0.3861 | 16800 | 0.7683 | - | | 0.3872 | 16850 | 0.6687 | - | | 0.3884 | 16900 | 0.5821 | - | | 0.3895 | 16950 | 0.6572 | - | | 0.3907 | 17000 | 0.9609 | - | | 0.3918 | 17050 | 0.0123 | - | | 0.3930 | 17100 | 0.5649 | - | | 0.3941 | 17150 | 0.1006 | - | | 0.3953 | 17200 | 0.003 | - | | 0.3964 | 17250 | 0.278 | - | | 0.3976 | 17300 | 0.8632 | - | | 0.3987 | 17350 | 0.5101 | - | | 0.3999 | 17400 | 0.8753 | - | | 0.4010 | 17450 | 0.3195 | - | | 0.4022 | 17500 | 0.9436 | - | | 0.4033 | 17550 | 0.9388 | - | | 0.4045 | 17600 | 0.0097 | - | | 0.4056 | 17650 | 0.6898 | - | | 0.4068 | 17700 | 0.035 | - | | 0.4079 | 17750 | 0.4828 | - | | 0.4091 | 17800 | 0.1888 | - | | 0.4102 | 17850 | 0.0354 | - | | 0.4114 | 17900 | 0.0008 | - | | 0.4125 | 17950 | 0.2885 | - | | 0.4137 | 18000 | 0.0624 | - | | 0.4148 | 18050 | 0.5545 | - | | 0.4160 | 18100 | 0.5317 | - | | 0.4171 | 18150 | 0.0207 | - | | 0.4183 | 18200 | 0.0228 | - | | 0.4194 | 18250 | 0.0168 | - | | 0.4206 | 18300 | 0.0935 | - | | 0.4217 | 18350 | 0.8391 | - | | 0.4229 | 18400 | 0.0005 | - | | 0.4240 | 18450 | 0.7018 | - | | 0.4252 | 18500 | 0.0137 | - | | 0.4263 | 18550 | 0.0053 | - | | 0.4275 | 18600 | 0.0307 | - | | 0.4286 | 18650 | 0.0127 | - | | 0.4298 | 18700 | 0.2351 | - | | 0.4309 | 18750 | 0.0047 | - | | 0.4321 | 18800 | 0.0114 | - | | 0.4332 | 18850 | 0.0153 | - | | 0.4344 | 18900 | 0.3732 | - | | 0.4355 | 18950 | 0.77 | - | | 0.4367 | 19000 | 0.1298 | - | | 0.4378 | 19050 | 0.7064 | - | | 0.4390 | 19100 | 0.0 | - | | 0.4401 | 19150 | 0.0044 | - | | 0.4413 | 19200 | 0.7627 | - | | 0.4424 | 19250 | 0.556 | - | | 0.4436 | 19300 | 0.2105 | - | | 0.4447 | 19350 | 0.8194 | - | | 0.4459 | 19400 | 0.027 | - | | 0.4470 | 19450 | 0.9308 | - | | 0.4482 | 19500 | 0.0194 | - | | 0.4493 | 19550 | 0.0144 | - | | 0.4505 | 19600 | 0.584 | - | | 0.4516 | 19650 | 0.0042 | - | | 0.4527 | 19700 | 0.1354 | - | | 0.4539 | 19750 | 0.2151 | - | | 0.4550 | 19800 | 0.0006 | - | | 0.4562 | 19850 | 0.3085 | - | | 0.4573 | 19900 | 0.0543 | - | | 0.4585 | 19950 | 0.0178 | - | | 0.4596 | 20000 | 0.418 | - | | 0.4608 | 20050 | 0.019 | - | | 0.4619 | 20100 | 0.0001 | - | | 0.4631 | 20150 | 0.5443 | - | | 0.4642 | 20200 | 0.5111 | - | | 0.4654 | 20250 | 0.0594 | - | | 0.4665 | 20300 | 0.0086 | - | | 0.4677 | 20350 | 0.0064 | - | | 0.4688 | 20400 | 0.0577 | - | | 0.4700 | 20450 | 0.0712 | - | | 0.4711 | 20500 | 0.0271 | - | | 0.4723 | 20550 | 0.5118 | - | | 0.4734 | 20600 | 0.1834 | - | | 0.4746 | 20650 | 0.0116 | - | | 0.4757 | 20700 | 0.0052 | - | | 0.4769 | 20750 | 0.7975 | - | | 0.4780 | 20800 | 0.3037 | - | | 0.4792 | 20850 | 0.0264 | - | | 0.4803 | 20900 | 0.6911 | - | | 0.4815 | 20950 | 0.008 | - | | 0.4826 | 21000 | 0.0041 | - | | 0.4838 | 21050 | 0.0379 | - | | 0.4849 | 21100 | 0.0033 | - | | 0.4861 | 21150 | 0.0297 | - | | 0.4872 | 21200 | 0.0147 | - | | 0.4884 | 21250 | 0.0001 | - | | 0.4895 | 21300 | 0.0047 | - | | 0.4907 | 21350 | 0.0247 | - | | 0.4918 | 21400 | 0.0059 | - | | 0.4930 | 21450 | 0.5724 | - | | 0.4941 | 21500 | 0.3113 | - | | 0.4953 | 21550 | 0.0026 | - | | 0.4964 | 21600 | 0.835 | - | | 0.4976 | 21650 | 0.0007 | - | | 0.4987 | 21700 | 0.029 | - | | 0.4999 | 21750 | 0.707 | - | | 0.5010 | 21800 | 0.0211 | - | | 0.5022 | 21850 | 0.0071 | - | | 0.5033 | 21900 | 0.0009 | - | | 0.5045 | 21950 | 0.0319 | - | | 0.5056 | 22000 | 0.2219 | - | | 0.5068 | 22050 | 0.0244 | - | | 0.5079 | 22100 | 0.0341 | - | | 0.5091 | 22150 | 0.0372 | - | | 0.5102 | 22200 | 0.3981 | - | | 0.5114 | 22250 | 0.0627 | - | | 0.5125 | 22300 | 0.0559 | - | | 0.5137 | 22350 | 0.5366 | - | | 0.5148 | 22400 | 0.6952 | - | | 0.5159 | 22450 | 0.0504 | - | | 0.5171 | 22500 | 0.5098 | - | | 0.5182 | 22550 | 0.6538 | - | | 0.5194 | 22600 | 0.0015 | - | | 0.5205 | 22650 | 0.0005 | - | | 0.5217 | 22700 | 0.0974 | - | | 0.5228 | 22750 | 0.009 | - | | 0.5240 | 22800 | 0.6559 | - | | 0.5251 | 22850 | 0.026 | - | | 0.5263 | 22900 | 0.0049 | - | | 0.5274 | 22950 | 0.0104 | - | | 0.5286 | 23000 | 0.7918 | - | | 0.5297 | 23050 | 0.0007 | - | | 0.5309 | 23100 | 0.0015 | - | | 0.5320 | 23150 | 0.2873 | - | | 0.5332 | 23200 | 0.002 | - | | 0.5343 | 23250 | 0.0067 | - | | 0.5355 | 23300 | 0.2943 | - | | 0.5366 | 23350 | 0.0029 | - | | 0.5378 | 23400 | 0.0 | - | | 0.5389 | 23450 | 0.0727 | - | | 0.5401 | 23500 | 0.0084 | - | | 0.5412 | 23550 | 0.0 | - | | 0.5424 | 23600 | 0.0054 | - | | 0.5435 | 23650 | 0.0004 | - | | 0.5447 | 23700 | 0.5525 | - | | 0.5458 | 23750 | 0.0251 | - | | 0.5470 | 23800 | 0.0269 | - | | 0.5481 | 23850 | 0.7426 | - | | 0.5493 | 23900 | 0.0016 | - | | 0.5504 | 23950 | 0.8143 | - | | 0.5516 | 24000 | 0.5158 | - | | 0.5527 | 24050 | 0.0047 | - | | 0.5539 | 24100 | 0.0067 | - | | 0.5550 | 24150 | 0.0 | - | | 0.5562 | 24200 | 0.0045 | - | | 0.5573 | 24250 | 0.0021 | - | | 0.5585 | 24300 | 0.0012 | - | | 0.5596 | 24350 | 0.3501 | - | | 0.5608 | 24400 | 0.0101 | - | | 0.5619 | 24450 | 0.0008 | - | | 0.5631 | 24500 | 0.0112 | - | | 0.5642 | 24550 | 0.0148 | - | | 0.5654 | 24600 | 0.2246 | - | | 0.5665 | 24650 | 0.1538 | - | | 0.5677 | 24700 | 0.0001 | - | | 0.5688 | 24750 | 0.0001 | - | | 0.5700 | 24800 | 0.1296 | - | | 0.5711 | 24850 | 0.0101 | - | | 0.5723 | 24900 | 0.0032 | - | | 0.5734 | 24950 | 0.0714 | - | | 0.5746 | 25000 | 0.0 | - | | 0.5757 | 25050 | 0.0886 | - | | 0.5769 | 25100 | 0.0003 | - | | 0.5780 | 25150 | 0.0041 | - | | 0.5792 | 25200 | 0.0151 | - | | 0.5803 | 25250 | 0.0099 | - | | 0.5814 | 25300 | 0.0008 | - | | 0.5826 | 25350 | 0.028 | - | | 0.5837 | 25400 | 0.1064 | - | | 0.5849 | 25450 | 0.0373 | - | | 0.5860 | 25500 | 0.5589 | - | | 0.5872 | 25550 | 0.2522 | - | | 0.5883 | 25600 | 0.8553 | - | | 0.5895 | 25650 | 0.0004 | - | | 0.5906 | 25700 | 0.6575 | - | | 0.5918 | 25750 | 0.0034 | - | | 0.5929 | 25800 | 0.7313 | - | | 0.5941 | 25850 | 0.8363 | - | | 0.5952 | 25900 | 0.0156 | - | | 0.5964 | 25950 | 0.0044 | - | | 0.5975 | 26000 | 0.1387 | - | | 0.5987 | 26050 | 0.0487 | - | | 0.5998 | 26100 | 0.001 | - | | 0.6010 | 26150 | 0.0004 | - | | 0.6021 | 26200 | 0.0071 | - | | 0.6033 | 26250 | 0.0012 | - | | 0.6044 | 26300 | 0.021 | - | | 0.6056 | 26350 | 0.0212 | - | | 0.6067 | 26400 | 0.8472 | - | | 0.6079 | 26450 | 0.5686 | - | | 0.6090 | 26500 | 0.0721 | - | | 0.6102 | 26550 | 0.0235 | - | | 0.6113 | 26600 | 0.0 | - | | 0.6125 | 26650 | 0.0098 | - | | 0.6136 | 26700 | 0.3805 | - | | 0.6148 | 26750 | 0.0525 | - | | 0.6159 | 26800 | 0.0139 | - | | 0.6171 | 26850 | 0.0011 | - | | 0.6182 | 26900 | 0.0013 | - | | 0.6194 | 26950 | 0.0058 | - | | 0.6205 | 27000 | 0.0581 | - | | 0.6217 | 27050 | 0.477 | - | | 0.6228 | 27100 | 0.0073 | - | | 0.6240 | 27150 | 0.0033 | - | | 0.6251 | 27200 | 0.0082 | - | | 0.6263 | 27250 | 0.0028 | - | | 0.6274 | 27300 | 0.0001 | - | | 0.6286 | 27350 | 0.0265 | - | | 0.6297 | 27400 | 0.097 | - | | 0.6309 | 27450 | 0.2339 | - | | 0.6320 | 27500 | 0.5429 | - | | 0.6332 | 27550 | 0.3859 | - | | 0.6343 | 27600 | 0.0116 | - | | 0.6355 | 27650 | 0.0006 | - | | 0.6366 | 27700 | 0.0018 | - | | 0.6378 | 27750 | 0.0197 | - | | 0.6389 | 27800 | 0.0085 | - | | 0.6401 | 27850 | 0.0 | - | | 0.6412 | 27900 | 0.0141 | - | | 0.6424 | 27950 | 0.1121 | - | | 0.6435 | 28000 | 0.0123 | - | | 0.6446 | 28050 | 0.3018 | - | | 0.6458 | 28100 | 0.7669 | - | | 0.6469 | 28150 | 0.6745 | - | | 0.6481 | 28200 | 0.4283 | - | | 0.6492 | 28250 | 0.0237 | - | | 0.6504 | 28300 | 0.8327 | - | | 0.6515 | 28350 | 0.1052 | - | | 0.6527 | 28400 | 0.4264 | - | | 0.6538 | 28450 | 0.6714 | - | | 0.6550 | 28500 | 0.0039 | - | | 0.6561 | 28550 | 0.0065 | - | | 0.6573 | 28600 | 0.0178 | - | | 0.6584 | 28650 | 0.3817 | - | | 0.6596 | 28700 | 0.0584 | - | | 0.6607 | 28750 | 0.0217 | - | | 0.6619 | 28800 | 0.0019 | - | | 0.6630 | 28850 | 0.4605 | - | | 0.6642 | 28900 | 0.0049 | - | | 0.6653 | 28950 | 0.0011 | - | | 0.6665 | 29000 | 0.569 | - | | 0.6676 | 29050 | 0.0 | - | | 0.6688 | 29100 | 0.0874 | - | | 0.6699 | 29150 | 0.5388 | - | | 0.6711 | 29200 | 0.4093 | - | | 0.6722 | 29250 | 0.3076 | - | | 0.6734 | 29300 | 0.4542 | - | | 0.6745 | 29350 | 0.2569 | - | | 0.6757 | 29400 | 0.0155 | - | | 0.6768 | 29450 | 0.1146 | - | | 0.6780 | 29500 | 0.1341 | - | | 0.6791 | 29550 | 0.0304 | - | | 0.6803 | 29600 | 0.0095 | - | | 0.6814 | 29650 | 0.443 | - | | 0.6826 | 29700 | 0.5068 | - | | 0.6837 | 29750 | 0.024 | - | | 0.6849 | 29800 | 0.0079 | - | | 0.6860 | 29850 | 0.1769 | - | | 0.6872 | 29900 | 0.0001 | - | | 0.6883 | 29950 | 0.0104 | - | | 0.6895 | 30000 | 0.4234 | - | | 0.6906 | 30050 | 0.0042 | - | | 0.6918 | 30100 | 0.3934 | - | | 0.6929 | 30150 | 0.0119 | - | | 0.6941 | 30200 | 0.0012 | - | | 0.6952 | 30250 | 0.4434 | - | | 0.6964 | 30300 | 0.6101 | - | | 0.6975 | 30350 | 0.3655 | - | | 0.6987 | 30400 | 0.168 | - | | 0.6998 | 30450 | 0.8202 | - | | 0.7010 | 30500 | 0.0906 | - | | 0.7021 | 30550 | 0.0287 | - | | 0.7033 | 30600 | 0.3671 | - | | 0.7044 | 30650 | 0.7084 | - | | 0.7056 | 30700 | 0.3632 | - | | 0.7067 | 30750 | 0.0027 | - | | 0.7079 | 30800 | 0.0451 | - | | 0.7090 | 30850 | 0.3421 | - | | 0.7101 | 30900 | 0.0077 | - | | 0.7113 | 30950 | 0.0404 | - | | 0.7124 | 31000 | 0.7512 | - | | 0.7136 | 31050 | 0.2898 | - | | 0.7147 | 31100 | 0.0721 | - | | 0.7159 | 31150 | 0.009 | - | | 0.7170 | 31200 | 0.0474 | - | | 0.7182 | 31250 | 0.0041 | - | | 0.7193 | 31300 | 0.0249 | - | | 0.7205 | 31350 | 0.3519 | - | | 0.7216 | 31400 | 0.0936 | - | | 0.7228 | 31450 | 0.0049 | - | | 0.7239 | 31500 | 0.0035 | - | | 0.7251 | 31550 | 0.0296 | - | | 0.7262 | 31600 | 0.0264 | - | | 0.7274 | 31650 | 0.5318 | - | | 0.7285 | 31700 | 0.0029 | - | | 0.7297 | 31750 | 0.7741 | - | | 0.7308 | 31800 | 0.0807 | - | | 0.7320 | 31850 | 0.0154 | - | | 0.7331 | 31900 | 0.0181 | - | | 0.7343 | 31950 | 0.7881 | - | | 0.7354 | 32000 | 0.2723 | - | | 0.7366 | 32050 | 0.0549 | - | | 0.7377 | 32100 | 0.0198 | - | | 0.7389 | 32150 | 0.0083 | - | | 0.7400 | 32200 | 0.4985 | - | | 0.7412 | 32250 | 0.0111 | - | | 0.7423 | 32300 | 0.0057 | - | | 0.7435 | 32350 | 0.0393 | - | | 0.7446 | 32400 | 0.0786 | - | | 0.7458 | 32450 | 0.1888 | - | | 0.7469 | 32500 | 0.0382 | - | | 0.7481 | 32550 | 0.5611 | - | | 0.7492 | 32600 | 0.0749 | - | | 0.7504 | 32650 | 0.0064 | - | | 0.7515 | 32700 | 0.0002 | - | | 0.7527 | 32750 | 0.0159 | - | | 0.7538 | 32800 | 0.025 | - | | 0.7550 | 32850 | 0.0271 | - | | 0.7561 | 32900 | 0.251 | - | | 0.7573 | 32950 | 0.0002 | - | | 0.7584 | 33000 | 0.1407 | - | | 0.7596 | 33050 | 0.1596 | - | | 0.7607 | 33100 | 0.0069 | - | | 0.7619 | 33150 | 0.0655 | - | | 0.7630 | 33200 | 0.0435 | - | | 0.7642 | 33250 | 0.0032 | - | | 0.7653 | 33300 | 0.1908 | - | | 0.7665 | 33350 | 0.4326 | - | | 0.7676 | 33400 | 0.1699 | - | | 0.7688 | 33450 | 0.005 | - | | 0.7699 | 33500 | 0.4937 | - | | 0.7711 | 33550 | 0.0635 | - | | 0.7722 | 33600 | 0.0042 | - | | 0.7733 | 33650 | 0.0001 | - | | 0.7745 | 33700 | 0.0088 | - | | 0.7756 | 33750 | 0.0313 | - | | 0.7768 | 33800 | 0.0072 | - | | 0.7779 | 33850 | 0.0291 | - | | 0.7791 | 33900 | 0.0037 | - | | 0.7802 | 33950 | 0.0192 | - | | 0.7814 | 34000 | 0.0017 | - | | 0.7825 | 34050 | 0.0006 | - | | 0.7837 | 34100 | 0.0119 | - | | 0.7848 | 34150 | 0.1647 | - | | 0.7860 | 34200 | 0.009 | - | | 0.7871 | 34250 | 0.0004 | - | | 0.7883 | 34300 | 0.5268 | - | | 0.7894 | 34350 | 0.0523 | - | | 0.7906 | 34400 | 0.0537 | - | | 0.7917 | 34450 | 0.1654 | - | | 0.7929 | 34500 | 0.0003 | - | | 0.7940 | 34550 | 0.0021 | - | | 0.7952 | 34600 | 0.0016 | - | | 0.7963 | 34650 | 0.0002 | - | | 0.7975 | 34700 | 0.0001 | - | | 0.7986 | 34750 | 0.0001 | - | | 0.7998 | 34800 | 0.0204 | - | | 0.8009 | 34850 | 0.0047 | - | | 0.8021 | 34900 | 0.2942 | - | | 0.8032 | 34950 | 0.0039 | - | | 0.8044 | 35000 | 0.0237 | - | | 0.8055 | 35050 | 0.0002 | - | | 0.8067 | 35100 | 0.0009 | - | | 0.8078 | 35150 | 0.7804 | - | | 0.8090 | 35200 | 0.0012 | - | | 0.8101 | 35250 | 0.0303 | - | | 0.8113 | 35300 | 0.0265 | - | | 0.8124 | 35350 | 0.0071 | - | | 0.8136 | 35400 | 0.0053 | - | | 0.8147 | 35450 | 0.068 | - | | 0.8159 | 35500 | 0.0233 | - | | 0.8170 | 35550 | 0.4748 | - | | 0.8182 | 35600 | 0.0253 | - | | 0.8193 | 35650 | 0.0 | - | | 0.8205 | 35700 | 0.2029 | - | | 0.8216 | 35750 | 0.0063 | - | | 0.8228 | 35800 | 0.0179 | - | | 0.8239 | 35850 | 0.0039 | - | | 0.8251 | 35900 | 0.0123 | - | | 0.8262 | 35950 | 0.3021 | - | | 0.8274 | 36000 | 0.0096 | - | | 0.8285 | 36050 | 0.3735 | - | | 0.8297 | 36100 | 0.0281 | - | | 0.8308 | 36150 | 0.0612 | - | | 0.8320 | 36200 | 0.028 | - | | 0.8331 | 36250 | 0.6296 | - | | 0.8343 | 36300 | 0.1161 | - | | 0.8354 | 36350 | 0.0249 | - | | 0.8366 | 36400 | 0.0 | - | | 0.8377 | 36450 | 0.4144 | - | | 0.8388 | 36500 | 0.1574 | - | | 0.8400 | 36550 | 0.0083 | - | | 0.8411 | 36600 | 0.0385 | - | | 0.8423 | 36650 | 0.4681 | - | | 0.8434 | 36700 | 0.0628 | - | | 0.8446 | 36750 | 0.0005 | - | | 0.8457 | 36800 | 0.2092 | - | | 0.8469 | 36850 | 0.009 | - | | 0.8480 | 36900 | 0.031 | - | | 0.8492 | 36950 | 0.3659 | - | | 0.8503 | 37000 | 0.0003 | - | | 0.8515 | 37050 | 0.0117 | - | | 0.8526 | 37100 | 0.0061 | - | | 0.8538 | 37150 | 0.0163 | - | | 0.8549 | 37200 | 0.0 | - | | 0.8561 | 37250 | 0.0668 | - | | 0.8572 | 37300 | 0.0108 | - | | 0.8584 | 37350 | 0.1344 | - | | 0.8595 | 37400 | 0.0196 | - | | 0.8607 | 37450 | 0.0006 | - | | 0.8618 | 37500 | 0.0005 | - | | 0.8630 | 37550 | 0.45 | - | | 0.8641 | 37600 | 0.0002 | - | | 0.8653 | 37650 | 0.0032 | - | | 0.8664 | 37700 | 0.0035 | - | | 0.8676 | 37750 | 0.1411 | - | | 0.8687 | 37800 | 0.007 | - | | 0.8699 | 37850 | 0.0015 | - | | 0.8710 | 37900 | 0.6745 | - | | 0.8722 | 37950 | 0.0002 | - | | 0.8733 | 38000 | 0.2138 | - | | 0.8745 | 38050 | 0.0092 | - | | 0.8756 | 38100 | 0.4335 | - | | 0.8768 | 38150 | 0.0011 | - | | 0.8779 | 38200 | 0.0265 | - | | 0.8791 | 38250 | 0.6394 | - | | 0.8802 | 38300 | 0.3108 | - | | 0.8814 | 38350 | 0.1918 | - | | 0.8825 | 38400 | 0.0006 | - | | 0.8837 | 38450 | 0.0075 | - | | 0.8848 | 38500 | 0.5738 | - | | 0.8860 | 38550 | 0.008 | - | | 0.8871 | 38600 | 0.0043 | - | | 0.8883 | 38650 | 0.7087 | - | | 0.8894 | 38700 | 0.0044 | - | | 0.8906 | 38750 | 0.0045 | - | | 0.8917 | 38800 | 0.0009 | - | | 0.8929 | 38850 | 0.0118 | - | | 0.8940 | 38900 | 0.2812 | - | | 0.8952 | 38950 | 0.0581 | - | | 0.8963 | 39000 | 0.0016 | - | | 0.8975 | 39050 | 0.0284 | - | | 0.8986 | 39100 | 0.0061 | - | | 0.8998 | 39150 | 0.13 | - | | 0.9009 | 39200 | 0.0061 | - | | 0.9021 | 39250 | 0.0508 | - | | 0.9032 | 39300 | 0.214 | - | | 0.9043 | 39350 | 0.0032 | - | | 0.9055 | 39400 | 0.0234 | - | | 0.9066 | 39450 | 0.0318 | - | | 0.9078 | 39500 | 0.003 | - | | 0.9089 | 39550 | 0.3719 | - | | 0.9101 | 39600 | 0.0092 | - | | 0.9112 | 39650 | 0.0027 | - | | 0.9124 | 39700 | 0.3007 | - | | 0.9135 | 39750 | 0.0535 | - | | 0.9147 | 39800 | 0.0027 | - | | 0.9158 | 39850 | 0.8316 | - | | 0.9170 | 39900 | 0.3543 | - | | 0.9181 | 39950 | 0.7228 | - | | 0.9193 | 40000 | 0.4475 | - | | 0.9204 | 40050 | 0.0044 | - | | 0.9216 | 40100 | 0.0077 | - | | 0.9227 | 40150 | 0.0668 | - | | 0.9239 | 40200 | 0.0036 | - | | 0.9250 | 40250 | 0.0032 | - | | 0.9262 | 40300 | 0.035 | - | | 0.9273 | 40350 | 0.011 | - | | 0.9285 | 40400 | 0.0 | - | | 0.9296 | 40450 | 0.5078 | - | | 0.9308 | 40500 | 0.0003 | - | | 0.9319 | 40550 | 0.0 | - | | 0.9331 | 40600 | 0.0 | - | | 0.9342 | 40650 | 0.0029 | - | | 0.9354 | 40700 | 0.0001 | - | | 0.9365 | 40750 | 0.0003 | - | | 0.9377 | 40800 | 0.2938 | - | | 0.9388 | 40850 | 0.0059 | - | | 0.9400 | 40900 | 0.0646 | - | | 0.9411 | 40950 | 0.0067 | - | | 0.9423 | 41000 | 0.001 | - | | 0.9434 | 41050 | 0.7928 | - | | 0.9446 | 41100 | 0.0013 | - | | 0.9457 | 41150 | 0.0271 | - | | 0.9469 | 41200 | 0.0322 | - | | 0.9480 | 41250 | 0.0127 | - | | 0.9492 | 41300 | 0.0 | - | | 0.9503 | 41350 | 0.4948 | - | | 0.9515 | 41400 | 0.0185 | - | | 0.9526 | 41450 | 0.4775 | - | | 0.9538 | 41500 | 0.0046 | - | | 0.9549 | 41550 | 0.0002 | - | | 0.9561 | 41600 | 0.352 | - | | 0.9572 | 41650 | 0.5607 | - | | 0.9584 | 41700 | 0.0003 | - | | 0.9595 | 41750 | 0.1911 | - | | 0.9607 | 41800 | 0.0117 | - | | 0.9618 | 41850 | 0.0008 | - | | 0.9630 | 41900 | 0.0029 | - | | 0.9641 | 41950 | 0.0034 | - | | 0.9653 | 42000 | 0.0128 | - | | 0.9664 | 42050 | 0.3599 | - | | 0.9675 | 42100 | 0.5342 | - | | 0.9687 | 42150 | 0.0333 | - | | 0.9698 | 42200 | 0.0358 | - | | 0.9710 | 42250 | 0.0039 | - | | 0.9721 | 42300 | 0.0001 | - | | 0.9733 | 42350 | 0.0066 | - | | 0.9744 | 42400 | 0.0006 | - | | 0.9756 | 42450 | 0.0005 | - | | 0.9767 | 42500 | 0.5468 | - | | 0.9779 | 42550 | 0.0121 | - | | 0.9790 | 42600 | 0.0833 | - | | 0.9802 | 42650 | 0.0152 | - | | 0.9813 | 42700 | 0.001 | - | | 0.9825 | 42750 | 0.0074 | - | | 0.9836 | 42800 | 0.8221 | - | | 0.9848 | 42850 | 0.0039 | - | | 0.9859 | 42900 | 0.1647 | - | | 0.9871 | 42950 | 0.0014 | - | | 0.9882 | 43000 | 0.0006 | - | | 0.9894 | 43050 | 0.0008 | - | | 0.9905 | 43100 | 0.0 | - | | 0.9917 | 43150 | 0.1409 | - | | 0.9928 | 43200 | 0.0004 | - | | 0.9940 | 43250 | 0.0006 | - | | 0.9951 | 43300 | 0.0634 | - | | 0.9963 | 43350 | 0.1843 | - | | 0.9974 | 43400 | 0.0133 | - | | 0.9986 | 43450 | 0.2553 | - | | 0.9997 | 43500 | 0.0005 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.5.1 - Transformers: 4.38.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.18.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```